{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "461fa76e-f773-4234-a7bf-560198d8d063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w= [1702.22424242] b= [-922.33333333]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])\n",
    "y=np.array([[2000],[2200],[4900],[3221],[6834],[10567],[9566],[15678],[13644],[15789]])\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"w=\",model.coef_[0],\"b=\",model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "273578d9-bbbf-4db1-90fe-04c6e18866a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y2=model.predict(x)\n",
    "plt.xlabel('工龄/年')\n",
    "plt.ylabel('平均工资/元')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([0,11,1900,20000])\n",
    "plt.scatter(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y2,'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a61454f-6662-43cb-a38e-908451ecb387",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
